Incidence, Mortality, and Sex Differences of Non‐Valvular Atrial Fibrillation: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: It is unclear whether the incidence of non-valvular atrial fibrillation (NVAF) has been increasing over time. We aimed to provide contemporary estimates of the incidence and case-fatality of NVAF in a well-defined population. METHODS AND RESULTS: We used the computerized databases of the Régie de l'assurance maladie du Québec (RAMQ), responsible for administering the universal health care services for all its residents, to identify a population-based cohort of 243 800 patients with an incident diagnosis of NVAF during 2000-2009. The incidence rate of NVAF, age- and sex-standardized to the 2004 population of Québec, was 32.4 (95% confidence interval, 32.3 to 32.5) per 10 000 per year. There was no evidence of an increasing incidence of NVAF during the 10-year study period. The incidence rate was higher in men compared with women (age-adjusted incidence rate ratio 1.51; 95% CI 1.50 to 1.52). The 30-day case-fatality was 9.2% (95% CI 9.0 to 9.3), higher for men (10.0%; 95% CI 9.8 to 10.1) than women (8.5; 95% CI 8.3 to 8.6), and increasing with age, ranging from around 1% for cases aged <40 years to around 16% for cases aged ≥80. CONCLUSION: Current incidence estimates illustrate the significant burden of NVAF. Mortality remains particularly high around the time of diagnosis, and case-fatality increases with age, being systematically higher in men than women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".